Crowdsourcing-Based Road Surface Evaluation and Indexing

Abstract

Continuous monitoring of road surface quality is necessary to maintain high functionality of the entire road networks and allow users to perceive road roughness. Although several fundamental methods have been used to evaluate road roughness by employing smart probe cars, the accuracy of these methods is affected by factors, such as vehicle speed, sensor positions, and vehicle suspension systems. Also, the long-term application of these methods to the entire road network is also limited due to the high cost. Further, the widely used roughness indices do not reflect road users’ perception about road roughness as these indices are also affected by the accuracy of the roughness evaluation methods. Therefore, we propose a crowdsourcing based road roughness evaluation model which uses power spectral density accompanied with blind source separation technique to eliminate the vehicle effects. We also propose a road surface ranking based on majority voting algorithm for comparing roads based on their surface quality. Finally, the road surface roughness index is derived to widen the range of quality scale and capture roughness at fine granularity. The models are tested by real world experiments on different roads in Taiwan. The results show that the proposed model can accurately measure the roughness of roads, rank these roads and index them accordingly in a way that shows tiny differences in the road surface quality.